The subject matter disclosed herein relates to tomographic reconstruction, and in particular to the use of deep learning techniques to reconstruct data, such as projection or other scan-type data, into diagnostically or clinically useful images, including cross-sectional images and/or volumetric representations.
Non-invasive imaging technologies allow images of the internal structures or features of a patient/object to be obtained without performing an invasive procedure on the patient/object. In particular, such non-invasive imaging technologies rely on various physical principles (such as the differential transmission of X-rays through the target volume, the reflection of acoustic waves within the volume, the paramagnetic properties of different tissues and materials within the volume, the breakdown of targeted radionuclides within the body, and so forth) to acquire data and to construct images or otherwise represent the observed internal features of the patient/object.
All reconstruction algorithms are subject to various trade-offs, such as between computational efficiency, patient dose, scanning speed, image quality, and artifacts. Therefore, there is a need for reconstruction techniques that may provide improved benefits, such as increased reconstruction efficiency or speed, while still achieving good image quality or allowing a low patient dose.